Image_Scene_Classification_inference.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Image Scene Classification- inference.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0vn1vdMv9T5"
      },
      "source": [
        "!wget https://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_validation.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yumppaDDvdAm"
      },
      "source": [
        "import shutil\n",
        "shutil.unpack_archive(\"/content/mobilenet_v2_mai.tar.gz\", \"/content/\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1Rd5ySRvMv6",
        "outputId": "ef4125c1-ad3a-41e1-8135-86c39bc95fd9"
      },
      "source": [
        "!wget https://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_validation.zip"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-03-01 07:48:06--  https://data.vision.ee.ethz.ch/ihnatova/camera_scene_detection_validation.zip\n",
            "Resolving data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)... 129.132.52.178, 2001:67c:10ec:36c2::178\n",
            "Connecting to data.vision.ee.ethz.ch (data.vision.ee.ethz.ch)|129.132.52.178|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20050014 (19M) [application/zip]\n",
            "Saving to: ‘camera_scene_detection_validation.zip’\n",
            "\n",
            "camera_scene_detect 100%[===================>]  19.12M  15.6MB/s    in 1.2s    \n",
            "\n",
            "2021-03-01 07:48:08 (15.6 MB/s) - ‘camera_scene_detection_validation.zip’ saved [20050014/20050014]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kT5lmKNdwjEC",
        "outputId": "ae3ffabb-81d4-4c55-8b23-6b150aaefb8f"
      },
      "source": [
        "!unzip /content/camera_scene_detection_validation.zip"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  /content/camera_scene_detection_validation.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNPeVf-UvkeU"
      },
      "source": [
        "TF_LITE_FILE = 'mobilenetv2_int.tflite'"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GaBiAjZtwxPH"
      },
      "source": [
        "import tensorflow as tf"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ywJOtTRw9El",
        "outputId": "5a6e5d74-7c71-4973-f4d5-101dab381849"
      },
      "source": [
        "import os\n",
        "print(len(os.listdir('images/')))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "600\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hrXhasqH1JXG"
      },
      "source": [
        "import cv2\n",
        "\n",
        "images = []\n",
        "size = 224,224\n",
        "\n",
        "for image in os.listdir('images/'):\n",
        "    temp_img = cv2.imread('images/' + image)\n",
        "    temp_img = cv2.resize(temp_img, size)\n",
        "    images.append(temp_img)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gC0AgwcX1q7_"
      },
      "source": [
        "import numpy as np\n",
        "images = np.array(images)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X9iZ0Hq719DJ",
        "outputId": "0db03622-8960-42f3-8a15-84903f6cb516"
      },
      "source": [
        "print(images.shape)\n",
        "print(images.dtype)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(600, 224, 224, 3)\n",
            "uint8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_Gppng-wn-W",
        "outputId": "faf0ca53-58ab-4519-fee3-465fbcc551af"
      },
      "source": [
        "interpreter = tf.lite.Interpreter(model_path = TF_LITE_FILE)\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "\n",
        "print(\"Input Shape:\", input_details[0]['shape'])\n",
        "print(\"Input Type:\", input_details[0]['dtype'])\n",
        "print(\"Output Shape:\", output_details[0]['shape'])\n",
        "print(\"Output Type:\", output_details[0]['dtype'])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input Shape: [  1 224 224   3]\n",
            "Input Type: <class 'numpy.uint8'>\n",
            "Output Shape: [ 1 30]\n",
            "Output Type: <class 'numpy.uint8'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0Ffow55Swve2",
        "outputId": "099711b2-4a13-426d-b118-c58bb3b8e28e"
      },
      "source": [
        "interpreter.resize_tensor_input(input_details[0]['index'], (600, 224, 224, 3))\n",
        "interpreter.resize_tensor_input(output_details[0]['index'], (600, 30))\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()\n",
        "\n",
        "print(\"Input Shape:\", input_details[0]['shape'])\n",
        "print(\"Input Type:\", input_details[0]['dtype'])\n",
        "print(\"Output Shape:\", output_details[0]['shape'])\n",
        "print(\"Output Type:\", output_details[0]['dtype'])"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Input Shape: [600 224 224   3]\n",
            "Input Type: <class 'numpy.uint8'>\n",
            "Output Shape: [600  30]\n",
            "Output Type: <class 'numpy.uint8'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBE8yhg32FgI",
        "outputId": "b7313f0e-bf90-43a2-b9d3-77bec61556ef"
      },
      "source": [
        "interpreter.set_tensor(input_details[0]['index'], images)\n",
        "interpreter.invoke()\n",
        "\n",
        "tflite_model_predictions = interpreter.get_tensor(output_details[0]['index'])\n",
        "\n",
        "print(\"Prediction results shape:\", tflite_model_predictions.shape)\n",
        "prediction_classes = np.argmax(tflite_model_predictions, axis=1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction results shape: (600, 30)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fqWAorGP2Kr3",
        "outputId": "bfff7978-ab72-43de-b9d4-ea75f0999963"
      },
      "source": [
        "prediction_classes"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15, 29, 15,  8, 22,  5, 25, 23,  6,  1,  7,  5,  4, 21, 20,  3,  1,\n",
              "        3, 16, 24,  3,  2, 19, 27, 27, 15,  0, 28, 23, 11, 19,  7, 17,  0,\n",
              "       20, 12,  6, 23, 11, 23, 17, 20, 10, 14, 26, 20,  1, 17, 25,  9, 18,\n",
              "        8, 13, 13,  1, 22, 17,  1,  3, 21,  5, 29, 17, 21,  5, 15, 24, 24,\n",
              "       27, 12,  9,  1,  9, 17, 16, 11, 27, 19, 13,  5, 19, 12, 26, 23, 26,\n",
              "       22, 16, 25, 14, 29,  3, 22, 29,  7, 10, 19,  8, 17, 28,  2, 28,  4,\n",
              "       27, 23, 29,  2,  0, 20,  9,  4, 17, 16,  9,  6, 24,  3, 29, 25, 18,\n",
              "       24, 15,  3,  7, 17, 13, 25, 18, 13,  1,  3, 22, 13, 18, 10, 16, 17,\n",
              "       26, 11,  6,  4, 16,  5, 13, 13, 22, 15, 27,  0, 14,  7, 16, 25, 10,\n",
              "        3, 10, 23, 15, 10, 12, 23,  3, 14, 21,  8, 15, 26, 12, 26, 20, 28,\n",
              "        1, 11,  3, 19, 26,  4, 15, 21, 23, 26,  4, 13, 26, 21, 18, 28, 25,\n",
              "       22,  6, 11,  5, 12, 19, 20, 24, 13, 26, 24, 15, 24, 13,  4,  9, 21,\n",
              "        3,  3, 17, 20, 27, 26, 12,  4, 12,  4, 18,  6,  0, 26, 18,  2, 19,\n",
              "       25,  6, 18, 25,  2, 18, 10,  1,  1, 24, 20, 24,  9,  0, 27,  3,  3,\n",
              "        7,  6,  2, 17,  5,  2,  7, 13,  1, 13, 22, 27,  0,  8, 15, 18, 22,\n",
              "       27,  2, 23,  6, 13,  0,  0, 23,  1,  1, 19, 13, 22, 16, 16, 22, 24,\n",
              "        8, 19,  0, 14, 18,  8, 22, 16,  6,  4,  4, 17, 16, 10, 11, 18, 23,\n",
              "       14, 29, 20,  1, 19,  8, 20,  5, 13, 13, 29, 10, 10, 26,  1,  8, 14,\n",
              "        1, 28,  6, 21,  7, 23,  3, 29, 21,  5,  1,  0,  3,  2, 16,  2, 17,\n",
              "        9,  1, 20, 25, 25, 17,  8, 14, 24, 25,  1, 15,  2,  7, 20, 14, 16,\n",
              "       13, 15,  0,  9, 26, 25, 17, 10,  5, 29,  5,  4,  0, 28,  3,  9,  2,\n",
              "       14,  4,  5,  7, 14, 24, 20, 22, 22,  5,  2,  5, 20, 19, 10,  4, 18,\n",
              "       26, 29, 12, 28, 12, 23, 21, 28, 12, 16, 17, 17, 17,  3, 14,  3, 29,\n",
              "        6, 16, 19,  3, 18, 23, 11,  7, 16, 28, 12,  1,  4,  1, 26, 18,  2,\n",
              "       26, 27,  2, 25, 17, 16, 29,  1, 12, 15,  0,  1, 29, 21, 14,  4, 19,\n",
              "       14, 12, 15, 15, 20,  8, 24,  5, 17,  3,  9, 12,  4,  2, 25, 17, 24,\n",
              "       22, 29, 23, 10,  7,  4, 13,  9, 19, 10, 16, 26, 19, 10, 10,  4,  3,\n",
              "       18, 10, 19,  8, 25,  1, 29, 12, 16, 21, 11,  1,  5, 20, 19, 24, 12,\n",
              "        6, 21, 14, 20,  6, 18, 14, 18,  3, 20, 26, 14, 21,  1, 23,  2, 18,\n",
              "       23,  3, 24,  9,  7, 17,  6,  3, 29, 23, 18, 16,  3,  1, 25, 23, 13,\n",
              "        5, 28, 29, 25, 27, 23, 22, 28,  8, 28, 28,  9,  1, 21,  3,  4, 20,\n",
              "       23, 26,  5,  6,  4,  9,  5, 21,  0,  6, 23, 12, 12,  0,  7, 12, 26,\n",
              "       26, 23, 15, 25, 22, 21, 27, 22, 14, 11, 19, 18, 28,  0, 29, 21, 11,\n",
              "       28,  4, 17, 14, 14, 27, 27, 13, 13,  9,  8, 14,  6, 23, 24, 13, 16,\n",
              "        0, 15,  8,  3,  0, 11, 24, 26, 28, 28, 24, 18,  1, 19,  0, 22, 19,\n",
              "        3,  1,  7, 28,  0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wgVBoHai4GPr"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}